Auto-weighted collective matrix factorization with graph dual regularization for multi-view clustering

Multi-view clustering (MVC) is an attractive clustering paradigm that can incorporate comprehensive information from multiple views. Among the MVC schemes, collective matrix factorization (CMF) has shown its great power in extracting shared information of multi-view data. Based on CMF, we propose a novel unified MVC framework, named Auto-weighted Collective Matrix Factorization with Graph Dual Regularization (ACMF-GDR). Specifically, we assign adaptive weights for each view and incorporate the smoothing cluster structure learning term to construct a unified auto-weighted CMF for MVC. Our ACMF-GDR model can obtain the cluster labels and common representations of the samples in a one-step manner. Furthermore, to make the common representations discriminative, graph dual regularization terms with orthogonality constraints are adopted on multiple views to preserve the geometrical structure of the decomposed factors simultaneously. Experimental results show the superior clustering performance of the proposed method. (c) 2022 Elsevier B.V. All rights reserved.

Automated summary

Multi-view clustering is an attractive approach that combines information from multiple views. The collective matrix factorization (CMF) has been proved to be effective in extracting shared information for multi-view data. In this study, we propose a novel unified multi-view clustering framework, ACMF-GDR, which employs auto-weighted CMF with graph dual regularization. Experimental results demonstrate the superior performance of the proposed method in clustering.